Bridging the Green Tech Funding Gap: An AI-Driven Risk Assessment Framework — Evidence from the OBF project

The transition to a sustainable economy relies heavily on innovative environmental technologies. However, early-stage green tech startups frequently encounter the "Valley of Death" in financing, driven by high perceived technological risks and severe information asymmetries that deter traditional investors. This paper explores this funding gap through a qualitative case study: Ocean Bacterial Filtering (OBF), an innovative bioremediation technology designed to filter heavy metals from industrial discharge. We argue that the primary barrier to OBF's commercialization is not scientific feasibility, but the pricing of financial risk and operational uncertainty. To address this issue, the paper proposes a conceptual framework that integrates Explainable Artificial Intelligence (XAI) and Machine Learning into the venture capital risk assessment process. By utilizing predictive analytics to model biological stability and operational logistics, AI can reduce information asymmetry and provide investors with a quantifiable, transparent "Risk Score." This study contributes to the literature on impact investing and financial management by demonstrating how AI can transform complex environmental biotechnologies into investable assets, thereby accelerating capital allocation toward sustainable innovations.